






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX




def 				A		(					lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  list  )		-> float:
	UpperCamelCase__ :int						=      np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowercase__  )]  )
	UpperCamelCase__ :List[Any]						=      np.array(lowercase__  )
	UpperCamelCase__ :Dict						=      np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,		lowercase__  )  ) ,		x.transpose()  ) ,		lowercase__  )
	return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]  )




def 				A		(					lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  list  )		-> float:
	UpperCamelCase__ :Optional[int]						=      (1, 2, 1)
	UpperCamelCase__ :Optional[int]						=      (1, 1, 0, 7)
	UpperCamelCase__ :Optional[int]						=      SARIMAX(
	    lowercase__ ,		exog=lowercase__ ,		order=lowercase__ ,		seasonal_order=lowercase__  )
	UpperCamelCase__ :Tuple						=      model.fit(disp=lowercase__ ,		maxiter=600 ,		method="""nm"""  )
	UpperCamelCase__ :int						=      model_fit.predict(1 ,		len(lowercase__  ) ,		exog=[test_match]  )
	return result[0]




def 				A		(					lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  list  )		-> float:
	UpperCamelCase__ :Optional[Any]						=      SVR(kernel="""rbf""" ,		C=1 ,		gamma=0.1 ,		epsilon=0.1  )
	regressor.fit(lowercase__ ,		lowercase__  )
	UpperCamelCase__ :Optional[int]						=      regressor.predict(lowercase__  )
	return y_pred[0]




def 				A		(					lowercase__  :  list  )		-> float:
	train_user.sort()
	UpperCamelCase__ :List[Any]						=      np.percentile(lowercase__ ,		25  )
	UpperCamelCase__ :Optional[Any]						=      np.percentile(lowercase__ ,		75  )
	UpperCamelCase__ :Optional[int]						=      qa - qa
	UpperCamelCase__ :Tuple						=      qa - (iqr * 0.1)
	return low_lim




def 				A		(					lowercase__  :  list ,		lowercase__  :  float  )		-> bool:
	UpperCamelCase__ :List[Any]						=      0
	UpperCamelCase__ :str						=      0
	for i in list_vote:
		if i > actual_result:
			UpperCamelCase__ :List[str]						=      not_safe + 1
		else:
			if abs(abs(lowercase__  ) - abs(lowercase__  )  ) <= 0.1:
				safe += 1
			else:
				not_safe += 1
	return safe > not_safe


if __name__ == "__main__":
							# data_input_df = pd.read_csv("ex_data.csv", header=None)
							UpperCamelCase        =				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
							UpperCamelCase        =				pd.DataFrame(
							    data_input, columns=["total_user", "total_even", "days"]
							)
							UpperCamelCase        =				Normalizer().fit_transform(data_input_df.values)
							# split data
							UpperCamelCase        =				normalize_df[:, 2].tolist()
							UpperCamelCase        =				normalize_df[:, 0].tolist()
							UpperCamelCase        =				normalize_df[:, 1].tolist()

							# for svr (input variable = total date and total match)
							UpperCamelCase        =				normalize_df[:, [1, 2]].tolist()
							UpperCamelCase        =				x[: len(x) - 1]
							UpperCamelCase        =				x[len(x) - 1 :]

							# for linear regression & sarimax
							UpperCamelCase        =				total_date[: len(total_date) - 1]
							UpperCamelCase        =				total_user[: len(total_user) - 1]
							UpperCamelCase        =				total_match[: len(total_match) - 1]

							UpperCamelCase        =				total_date[len(total_date) - 1 :]
							UpperCamelCase        =				total_user[len(total_user) - 1 :]
							UpperCamelCase        =				total_match[len(total_match) - 1 :]

							# voting system with forecasting
							UpperCamelCase        =				[
							    linear_regression_prediction(
							        trn_date, trn_user, trn_match, tst_date, tst_match
							    ),
							    sarimax_predictor(trn_user, trn_match, tst_match),
							    support_vector_regressor(x_train, x_test, trn_user),
							]

							# check the safety of today's data
							UpperCamelCase        =				"" if data_safety_checker(res_vote, tst_user) else "not "
							print("Today's data is {not_str}safe.")


from ..utils import DummyObject, requires_backends







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
														requires_backends(self    ,						['''speech''']						)







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														requires_backends(self    ,						['''speech''']						)






